Author Archives:
Shreeharsh Kelkar

I am interested in understanding the role of computing, data, software and algorithms in institutions and workplaces using historical and ethnographic methods. More broadly, I am interested in the relationship between institutions, technology and knowledge production.

Editor’s Note: Today, Shreeharsh Kelkar brings us the inaugural post in a new series on Fake News and the Politics of Knowledge. The goal is to tackle the knowledge politics of both so-called “fake news” itself and the discourse that has cropped up around it, from a wide range of theoretical perspectives on media, science, technology, and communication. If you are interested in contributing, please write to editor@castac.org with a brief proposal.
Donald Trump’s shocking upset of Hillary Clinton in the 2016 US Presidential Election brought into wide prominence issues that heretofore had been debated mostly in intellectual and business circles: the question of “filter bubbles,” of people who refuse to accept facts (scientific or otherwise), and what these mean for liberal democracies and the public sphere. All these concerns have now have coalesced around an odd little signifier, “fake news” [1].
(read more...)

Most CASTAC readers familiar with science and technology studies (STS) have probably had conversations with friends—especially friends who are scientists or engineers—that go something like this: Your friend says that artificial intelligence (AI) is on its way, whether we want it or not. Programs (or robots, take your pick) will be able to do a lot of tasks that, until now, have always needed humans. You argue that it’s not so simple; that what we’re seeing is as much a triumph of re-arranging the world as it is of technological innovation. From your point of view, a world of ubiquitous software is being created; which draws on contingent, flexible, just-in-time, human labor; with pervasive interfaces between humans and programs that make one available to the other immediately. Your comments almost always get misinterpreted as a statement that the programs themselves are not really intelligent. Is that what you believe, your friend asks? How do you explain all those amazing robot videos then? “No, no,” you admit, “I am not saying there’s no technological innovation, but it’s complicated, you know.” Sometimes, at this point, it’s best to end the conversation and move on to other matters. (read more...)

How can we account for the radical uncertainty of change when we think about the future, but its seeming inevitability when it comes to the past? This is, arguably, the hardest part in doing the history and anthropology of technology. It is also, not surprisingly, the hardest to teach our students. In what follows, I suggest that the experience of watching (and playing) sports might be of help here. (read more...)

The meteoric rise of Bernie Sanders in the Democratic primaries—and the Occupy movement before that—have officially put income inequality on the political radar in the U.S., after years of slow wage growth and a near-catastrophic financial crash. In keeping with the times, Silicon Valley too has begun thinking about inequality. Resident philosopher Paul Graham, venture capitalist and founder of the famous YCombinator startup incubator, wrote an essay on inequality that caused a bit of a ruckus (in Silicon Valley and without).
The short version: Graham is not happy with the current rhetorical war on inequality that politicians are waging. He thinks inequality is a natural product of a culture that values startups and innovation, and that a full-scale political fight against inequality is inadvisable. YCombinator recently put out a “Request for Research” to sponsor social science research on Basic Income guarantee schemes. Such a scheme—Silicon Valley’s go-to solution for the rise of inequality and artificial intelligence—would mean every citizen receives a basic income that insulates them from the rise of automation and the progress of technology. (You can apply for the job here.)
In this post, I want to reflect on Silicon Valley’s political leanings, which allows me to bring in the fascinating political surveys of start-up founders that journalist Greg Ferenstein has conducted. There are some obvious (and important!) things to say about Graham’s essay and the Basic Income advertisement: that these writings take technology as an autonomous force that shapes society rather than seeing technological change as an outcome of negotiations between interest groups. They are articulations of very Silicon Valley notions of progress.
What I really want to talk about, however, is good old-fashioned electoral politics. In the kinds of political alliances and interest groups that will come to define the United States over the next few decades—perhaps as inequality takes an even bigger role in political discourse—could it be possible that Silicon Valley might be an ally for progressive causes rather than a foe (as it often emerges in critical theory analyses)? (read more...)

Numerous battles are being fought today within and across America’s political landscape, from global warming to the regulation of new technologies (e.g., GMOs, fracking). Science plays a big role in these debates, and as a result, social psychologists, political scientists, economists, and other social scientists have become interested in the question of why people (or rather, certain people) don’t accept scientific findings. These social scientists have converged on a concept called motivated reasoning: that because our reasoning powers are directed towards particular ends, we tend to pick facts that best fit our needs and motivations. Motivated reasoning, in this explanation, is a universal concept, perhaps a product of evolution; all human beings do it, including experts. It also raises the profoundly disturbing possibility of a scientific end to our Enlightenment hopes that experts—let alone publics—can be rational, that they can neatly separate facts from values and facilitate a harmonious society.
Influential science journalists have now started drawing on those findings. Chris Mooney, who made a name for himself writing The Republican War on Science, drew on social psychological and brain imaging research on political bias in a well-cited Mother Jones piece, “The Science of Why We Don’t Believe Science: How our brains fool us on climate, creationism, and the vaccine-autism link.” Other political scientists have written about this in high-profile outlets, such as Brendan Nyhan for the New York Times. It has also made several appearances on The Monkey Cage, a political science blog that is now part of the Washington Post. (read more...)

Alan Turing was involved in some of the most important developments of the twentieth century: he invented the abstraction now called the Universal Turing Machine that every undergraduate computer science major learns in college; he was involved in the great British Enigma code-breaking effort that deserves at least some credit for the Allied victory in World War II, and last, but not the least, while working on building early digital computers post-Enigma, he described — in a fascinating philosophical paper that continues to puzzle and excite to this day — the thing we now call the Turing Test for artificial intelligence. His career was ultimately cut short, however, after he was convicted in Britain of “gross indecency” (in effect for being gay), and two years later was found dead in an apparent suicide.
The celebrations of Turing’s birth centenary began three years ago in 2012. As a result, far, far more people now know about him than perhaps ever before. 2014 was probably the climax, since nothing is as consecrating as having an A-list Hollywood movie based on your life: a film with big-name actors that garners cultural prestige, decent press, and of course, an Academy Award. I highly recommend Christian Caryl’s review of the The Imitation Game (which covers Turing’s work in breaking the Enigma code). The film is so in thrall to the Cult of the Genius that it adopts a strategy not so much of humanizing Turing or giving us a glimpse of his life, but of co-opting the audience into feeling superior to the antediluvian, backward, not to mention homophobic, Establishment (here mostly represented by Tywin Lannister, I’m sorry, Commander Denniston). Every collective achievement, every breakthrough, every strategy, is credited to Turing, and to Turing alone. One scene from the film should give you a flavor of this: as his colleagues potter around trying to work out the Enigma encryption on pieces of paper, Turing, in a separate room all by himself, is shown to be building a Bombe (a massive, complicated, machine!) alone with his bare hands armed with a screwdriver!
The movie embodies a contradiction that one can also find in Turing’s life and work. On one hand, his work was enormously influential after his death: every computer science undergrad learns about the Turing Machine, and the lifetime achievement award of the premier organization of computer scientists is called the Turing Award. But on the other, he was relatively unknown while he lived (relatively being a key word here, since he studied at Cambridge and Princeton and crossed paths with minds ranging from Wittgenstein to John Von Neumann). Perhaps in an effort to change this, the movie (like many of his recent commemorations) goes all out in the opposite direction: it credits Turing with every single collective achievement, from being responsible for the entirety of the British code-breaking effort to inventing the modern computer and computer science. (read more...)

“Crowd” and “cloud” computing are exciting new technologies on the horizon, both for computer science types and also for us STS-types (science and technology studies, that is) who are interested in how different actors put them to (different) uses. Out of these, crowd computing is particularly interesting — as a technique that both improves artificial intelligence (AI) and operates to re-organize work and the workplace. In addition, as Lilly Irani shows, it also performs cultural work, producing the figure of the heroic problem-solving innovator. To this, I want to add a another point: might “human computation and crowdsourcing” (as its practitioners call it) be changing our widely-held ideas about experts and expertise?
Here’s why. I’m puzzled by how crowdsourcing research both valorizes expertise while at the same time sets about replacing the expert with a combination of programs and (non-expert) humans. I’m even more puzzled by how crowd computing experts rarely specify the nature of their own expertise; if crowdsourcing is about replacing experts, then what exactly are these “human computation” experts themselves experts on? Any thoughts, readers? How might we think about the figure of the expert in crowd computing research, given the recent surge of public interest in new forms of — and indeed fears about — this thing called artificial intelligence? (read more...)

Bruno Latour’s Science in Action remains an unparalleled introduction to science studies because of its conversational style and clever use of the conventions of the “how-to” genre. And Latour has other shorter, more pedagogical, articles that show wonderfully how non-living objects are deeply embedded in complex social relations. But I sometimes wonder if his examples–the door-closer, the speed-bump, or sometimes, even the gun — are too simple. I worry about teaching these examples to savvy undergraduates in an introductory STS class. Will they just laugh it off dismissing it as obvious? Will they look at it as philosophy, as a conceptual case, rather than as anthropology? Could there be a more immediate example where the politics is not abstract, but more concrete? Where the students can use the immediacy of their own experience, but also where the stakes are higher? (read more...)

The term “big data” [1] brings up the specter of a new positivism, as another one in the series of many ideological tropes that have sought to supplant the qualitative and descriptive sciences with numbers and statistics.[2]
But what do scientists think of big data? Last year, in a widely circulated blog post titled “The Big Data Brain Drain: Why Science is in Trouble,” physicist Jake VanderPlas made the argument that the real reason big data is dangerous is because it moves scientists from the academy to corporations. (read more...)

In the media these days, Artificial Intelligence (henceforth AI) is making a comeback. Kevin Drum wrote a long piece for Mother Jones about what the rising power of intelligent programs might mean for the political economy of the United States: for jobs, capital-labor relations, and the welfare state. He worries that as computer programs become more intelligent day by day, they will be put to more and more uses by capital, thereby displacing white-collar labor. And while this may benefit both capital and labor in the long run, the transition could be long and difficult, especially for labor, and exacerbate the already-increasing inequality in the United States. (For more in the same vein, here’s Paul Krugman, Moshe Vardi, Noah Smith, and a non-bylined Economist piece.) (read more...)